This study was built on PyTorch version 1.8.1 (https://pytorch.org/), and used a machine with an Nvidia GTX 1080 GPU. We trained the feature extractors using SGD optimizer [1]} with momentum = 0.9 and cosine annealing learning rate [2]}. The cross-entropy function was used to calculate the error. For model evaluation, we used F1-score on the 5-class BI-RADS level and 4-class density level. F1-score is the harmonic mean of precision and recall. For multi-class problems, macro-F1 score, which is defined as the mean of class-wise F1-scores could be used. As each study's left side and right side might have characteristic differences, the results are appraised for left breasts, right breasts, and study-level for breast diagnosis (BI-RADS types or pathology labels). The evaluation of density cases is based on the left, right, and side-level.
